Prevalence and factors associated with long COVID and mental health status among recovered COVID-19 patients in southern Thailand

Mental health disorders have become a growing public health concern among individuals recovering from COVID-19. Long COVID, a condition where symptoms persist for an extended period, can predict psychological problems among COVID-19 patients. This study aimed to investigate the prevalence of long COVID and mental health status among Thai adults who had recovered from COVID-19, identify the association between the mental health status and long COVID symptoms, and investigate the risk factors associated with the correlation between long COVID and mental health outcomes. A cross-sectional study was conducted among 939 randomly selected participants in Nakhon Si Thammarat province, southern Thailand. The Depression, Anxiety, and Stress Scale-21 was used to investigate mental health symptoms, and a checklist comprised of thirteen common symptoms was used to identify the long COVID among participants. Logistic regression models were used to investigate the risk factors associated with mental health status and long COVID symptoms among participants. Among the 939 participants, 104 (11.1%) had depression, 179 (19.1%) had anxiety, and 42 (4.8%) were stressed. A total of 745 participants (79.3%) reported experiencing at least one symptom of long COVID, with fatigue (72.9%, SE±0.02), cough (66.0%, SE±0.02), and muscle pain (54.1%, SE±0.02) being the most frequently reported symptoms. All long COVID symptoms were significantly associated with mental health status. Shortness of breath, fatigue, and chest tightness were the highest risk factors for mental health status among COVID-19 patients. The final multivariable model indicated that female patients (OR = 1.89), medical history (OR = 1.92), and monthly income lower than 5,000 Thai baht (OR = 2.09) were associated with developing long COVID symptoms and mental health status (all p<0.01). This study provides valuable insights into the potential long-term effects of COVID-19 on mental health and enhances understanding of the mechanisms underlying the condition for predicting the occurrence of mental health issues in Thai COVID-19 patients.


Introduction
Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 5 Mental health disorders have emerged as a pressing public health concern for individuals who have recovered from COVID-19. Previous studies suggested a potential link between the psychological symptoms of COVID-19 patients and the onset of long COVID. This highlights the importance of identifying the risk factors associated with mental health disorders and post-COVID-19 conditions.
Objectives 3 State specific objectives, including any prespecified hypotheses 6 The study aims to: (1) examine the prevalence of long COVID and mental health disorders among Thai adults who have recovered from COVID-19, (2) identify the association between mental health issues such as depression, anxiety and stress and long COVID symptoms among COVID-19 participants, (3) investigate the risk factors associated with the correlation between mental health outcomes and the onset of long COVID in adult patients who have previously contracted COVID-19

Study design 4
Present key elements of study design early in the paper

6-10
The critical elements of a study design early in a paper typically include the following information: -Study type: observational study (a cross-sectional study) -Participants: Thai adults who have recovered from COVID-19, were over 18 years old, had no mental health disorders diagnosed in the past, and got COVID-19 infection between January and May 2022.
-Study area: in a community setting in subdistricts of Sichon district, Nakhon Si Thammarat Province, southern Thailand.
-Data collection: a survey using a structured questionnaire and medical records.
-Variables: socio-demographic information, body mass index (BMI), medical history, checklist (Yes/No) of thirteen common long COVID symptoms; and Depression, Anxiety and Stress scores using DASS-21.
-Data analyses: Prevalence of long COVID, mental health disorders and characteristics of study participants were measured using descriptive analyses. Risk factors analyses were applied using logistic regression models.
Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection 7, 9 The study was conducted in the community setting across nine subdistricts of Sichon district, located in Nakhon Si Thammarat Province, southern Thailand. Only individuals who recovered from COVID-19 between January and May 2022 were selected to participate in our study. To ensure sufficient time had elapsed for long COVID conditions, the study was conducted in November 2022, which corresponds to three months after the patients contracted COVID-19 and at least two months of experiencing symptoms indicative of long COVID. Fourteen village health volunteers and study staff members were assigned to the nine subdistricts to collect data for the study. The study involved administering a structured questionnaire to the participants, which consisted of three sections.
-The first section aimed to collect socio-demographic information: age, gender, education level, marital status, occupation, and monthly income. Additional data were also collected on height, weight, underlying diseases of all participants. Body mass index (BMI) was calculated using the weight to-height squared (kg/m 2 ) ratio, and individuals were categorized as underweight, normal weight, overweight at risk, or obese, based on their BMI values (<18.5, 18.5-22.9, 23.0-24.9, and ≥ 25.0, respectively).
-The second section of the questionnaire consisted of 13 commonly reported symptoms associated with long COVID included fatigue, shortness of breath, chest tightness, palpitations, cough, amnesia, insomnia, joint pain, muscle pain, asthenia, significant hair loss, headache, and dizziness. The participants were asked "Yes/No" questions to indicate whether they had experienced these symptoms for a period of two months after three months infected with SARS-CoV-2. Those who reported experiencing at least one symptom were classified as having long COVID.
-The third section involved the use of the 21-item Depression Anxiety and Stress Scale (DASS-21) developed by Lovibond and Lovibond (1995). The DASS-21 was used to assess the emotional states of the participants in relation to the three mental health disorders: depression (7 items), anxiety (7 items), and stress (7 items). Each term was rated on a 4-point scale, ranging from "did not apply to me at all" (0 points), "applied to me some degree or some of the time" (1 point), "applied to me to a considerable degree or a good part of the time" (2 points), and "applied to me very much or most of the time" (3 points severe: 26 -33; extremely severe: ≥ 34). Participants were then categorized based on their scores as "normal", "mild", "moderate", "severe", and "extreme severe" for each disorder.
Participants who scored in the "mild" to "extremely severe" range were considered to have mental health disorders.

Bias 9
Describe any efforts to address potential sources of bias 7, 9, 10 Several sources of bias can affect observational studies. To address these potential biases, we took the following measures in our research: -Selection bias: We randomly selected study participants from a list of eligible individuals to minimize selection bias.
-Information bias: Our effort to minimize the information bias was as follows: First, we included only participants with no mental health disorders before contracting COVID-19.
Second, we used a standardized and structured questionnaire, evaluated for content validity, to help participants accurately remember and report their experiences. Third, we collected data in November 2022, the proper time after participants contracted COVID-19 (between January and May 2022). Shorter recall periods are recommended for data collection to reduce the chances of recall bias.
-Confounders: We used multivariable analyses to reduce our study's potential for confounding biases.
Study size 10 Explain how the study size was arrived at 7 The target sample size of approximately 900-950 representative participants was determined using a web-based sample size calculation tool (http://www.winepi.net). This calculation was based on a reported prevalence of 57% of COVID-19 survivors experiencing long COVID, a population size (N) of 9,396 from the hospital databases, a margin of error (d) of around 3%, and a confidence interval of 95%.
Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why 9,10 -For socio-demographic data: (age, gender, education level, marital status, occupation, monthly income, BMI): quantitative variables were grouped into categories and shown using percentage (%) for each type.
-Descriptive statistics were applied to calculate (prevalence, standard error) to summarize the prevalence of mental health disorders and long COVID symptoms among participants.
-Apart from calculating the frequency of each long COVID symptom, we calculated a standardized score for each COVID symptom in case the participant experienced more than one symptom. This score was calculated by multiplying the rank of each symptom by its share in each participant's observations, which totalled 100%. For example, if a participant mentioned 3 out of 13 symptoms, such as fatigue, shortness of breath, and chest tightness, each would receive a standardized score of 33.3%.
Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 9, 10 -The association between socio-demographic factors and long COVID among participants was examined using the Chi-squared test and Fisher's Exact test.
-In addition, we hypothesized that the mental health conditions of COVID-19 patients were likely to be associated with the symptoms of long COVID. To test this hypothesis, we used the odds ratio (OR) to examine the association between depression, anxiety and stress and each symptom of long COVID.
-Logistic regression was utilized to identify risk factors associated with mental health status and long COVID among COVID-19 patients. In order to perform logistic regression analysis, the two binary variables of mental health status and long COVID were merged into a new variable with four levels. A binary outcome variable was created for logistic regression modelling, where observations with experience of both mental health status and long COVID were categorized as "1", while the three other levels of the new variable were considered "0". Explainatory variables (predictors) included: (1) gender, (2) age, (3) marital status, (4) education, (5) occupation, (6) monthly income, (7) BMI, and (8) medical history. Univariable models were initially screened for all explanatory variables, and those with P < 0.20 were selected as candidates for the final model. The multivariable model consisted of variables with P < 0.05 and was used to identify significant risk factors associated with mental health status and long COVID in COVID-19 patients.
Multivariable models are used in statistical analyses to control for potential confounders (b) Describe any methods used to examine subgroups and interactions 10 Interactions between all pairs of explanatory variables were examined to determine any potential confounding effects between the explanatory variables.
(c) Explain how missing data were addressed 7, Supportting Information Using a structured questionnaire, we ensured no data were missed during the data collection (please refer to the raw data attached in the Supporting Information). However, in the event of any missing data and considering the potential impact of such missing data, various approaches (i.e., complete case analysis, mean/mode imputation, or multiple imputations) may be applied.
(d) Cohort study-If applicable, explain how loss to follow-up was addressed 7 The sample size calculation was based on the population size (N) of 9,396 eligible participants (as a finite population). We selected individuals from a database of two secondary care hospitals and three field hospitals containing information on 10,336 participants who had recovered from COVID-19 between January and May 2022. Only individuals over 18 years old and with no history of mental health disorders before contracting COVID-19 were included in the study.
Ultimately, 9,396 eligible participants were deemed suitable to participate in our research.
To determine the target sample size, we used a web-based sample size calculation tool (http://www.winepi.net). Based on a reported prevalence of 57% of COVID-19 survivors experiencing long COVID, a population size (N) of 9,396 from the hospital databases, a margin of error (d) of approximately 3%, and a confidence interval of 95%, a representative sample size of roughly 900-950 participants was determined.
Proportional allocation using stratified sampling was employed to select participants from each subdistrict randomly. As a result, 939 participants were included in this study.
(b) Give reasons for non-participation at each stage In the case of participants being absent from their houses during data collection or refusing to participate in the study from the randomly selected list, a solution was implemented to address these situations. An additional participant was randomly recruited from a reserved list, ensuring adequate sample size was maintained for analysis. A total of 939 participants were included in this study, of whom 745 (79.3%) reported experiencing at least one symptom lasting for more than two months, indicating prolonged COVID-19 symptoms, while 194 participants (20.7%) reported no history of long COVID symptoms. The majority of respondents were female (77.4%), and a significant gender difference was observed in the development of long COVID (χ2 test, p < 0.001). Most participants were younger than 60 (84.7%), and no significant association was found between long COVID and age groups either under or over 60 years old (χ2 test, p = 0.867).
Approximately 69.1% of participants were currently married, and a significant difference was observed in the development of long COVID based on marital status (χ2 test, p = 0.021). The study found that around 80% of participants reported a high school education or lower, with self-employment being the most common occupation. However, no significant association was found between long COVID and education or occupation (χ2 test and Fisher's exact test, all p ≥ 0.168). A majority (90%) of study participants earned a monthly income of less than 15,000 Thai baht (~ 450 USD), and a significant association was found between long COVID symptoms and monthly income levels (χ2 test, p = 0.009). Sixty-five percent of participants were overweight (BMI > 23.0), and 44% were obese. The study found significant associations between increased BMI levels and long COVID symptoms.
While no significant associations were found between long COVID and most historical diseases reported by participants, such as hypertension, diabetes, cardiovascular disease, or allergies (all p ≥ 0.154), a significant association was observed for dyslipidemia status among participants (χ2 test, p < 0.001). The adjusted estimates of were represented in the univariable models in Table 3 with their precision (95% CI) and P-value.
(b) Report category boundaries when continuous variables were categorized Table   1 Only one continuous variable, "age", was categorized into two subsets (19-59 vs ≥ 60). We hypothesized the advanced age might be a risk factor for long COVID and mental health disorders. However, there was no significant association between the age of participants and long COVID and mental health disorders in our study.
(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period

Not applicable
Other analyses 17 Report other analyses doneeg analyses of subgroups and interactions, and sensitivity analyses 16 No significant interactions were found between potential explanatory variables in multivariable model.

Discussion
Key results 18 Summarise key results with reference to study objectives 18 Our study has revealed that depression, anxiety, and stress are not prevalent among COVID-19 adults after being discharged from the hospital. However, a high prevalence of long COVID symptoms was observed, with fatigue, cough and muscle pain being the most common. The study also identifies shortness of breath, fatigue, and chest tightness as the highest risk factors for mental health disorders among COVID-19 patients who experience such issues. Finally, the study finds that female patients, medical history of COVID-19 patients, and low income are associated with the development of long COVID symptoms and mental health disorders among the study participants. Sampling: While the study employed random selection to recruit participants from a specific province, its generalizability beyond the local area may be limited. Regional variations in demographics, healthcare access, and COVID-19 outcomes could affect the applicability of the findings to the broader Thai population or other countries. Furthermore, the study focused exclusively on rural communities in southern Thailand, which might lead to differences in the prevalence and factors associated with long COVID symptoms and mental health disorders in other regions. Although the findings may apply to rural areas, further research is necessary to understand the prevalence and factors related to long COVID and mental health issues among COVID-19 patients in various regions of Thailand.

Measurement Tools:
The study utilized the Depression, Anxiety, and Stress Scale-21 (DASS-21) to assess mental health disorders and a checklist of thirteen common symptoms to identify long COVID. These measurement tools have been widely used and validated, enhancing the reliability of the findings.

Risk Factors and Associations:
The study identified several risk factors associated with mental health disorders and long COVID symptoms, such as gender, medical history, and monthly income. While these findings provide insights into potential associations, the generalizability may depend on the similarity of risk factors across populations. Factors like healthcare access, cultural influences, and socioeconomic disparities may differ in other regions, which could affect the generalizability of the identified risk factors.

Contextual Considerations:
The study was conducted in a specific timeframe and geographical location. The generalizability of the findings may be influenced by the circumstances prevailing during the study period, such as the local COVID-19 situation, healthcare infrastructure, and public health measures. When applying the results to other settings or time periods, these contextual factors should be considered.

Consistency with Previous Studies:
The results of this study are consistent with prior research that has highlighted the impact of COVID-19 on mental health and the persistence of long COVID symptoms. *Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Other information
Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.